{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Difference Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook explores the calculations needed to plot the Difference Map in the Project Dashboard  \n",
    "**Create multiple scenarios:** create some dummy scenario data for comparisons  \n",
    "**Difference Map function:** make the calculations needed for the Difference Map.  \n",
    "**Plot Difference map:** example of visualization with bivariate map\n",
    "\n",
    "The assumption is that each planning unit should fall into just one category.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "from shapely.geometry import Polygon\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "%run marxan_utils.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create multiple scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scenario 1\n",
    "MARXAN_FOLDER = '/home/jovyan/work/datasets/raw/Marxan_DiffMap/Scenario1'\n",
    "Input,pu,spec,puvsp,bound = validate_all_files(MARXAN_FOLDER,'input.dat')\n",
    "execute_marxan(MARXAN_FOLDER)\n",
    "#BLM 0.3, n = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scenario 2\n",
    "MARXAN_FOLDER = '/home/jovyan/work/datasets/raw/Marxan_DiffMap/Scenario2'\n",
    "MARXAN_INPUTDATA = 'input.dat'\n",
    "userInputFile,pu,spec,puvsp,bound = validate_all_files(MARXAN_FOLDER,MARXAN_INPUTDATA)\n",
    "\n",
    "### Modify BLM\n",
    "userInputFile.BLM = 10\n",
    "userInputFile.NUMREPS = 30\n",
    "userInputFile_df = pd.DataFrame.from_dict(userInputFile.__dict__, orient='index')\n",
    "userInputFile_df= userInputFile_df.drop('BLOCKDEFNAME')\n",
    "CreateFileFromDF(f'{MARXAN_FOLDER}/{MARXAN_INPUTDATA}',userInputFile_df,inputDatFile)\n",
    "\n",
    "### Modify SPF and prop\n",
    "spec.prop =0.5\n",
    "spec.spf = 1\n",
    "spec.head()\n",
    "CreateFileFromDF(f'{MARXAN_FOLDER}/{Input.INPUTDIR}/{Input.SPECNAME}', spec, conservationFeature)\n",
    "\n",
    "### Run Marxan\n",
    "execute_marxan(MARXAN_FOLDER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scenario 3\n",
    "MARXAN_FOLDER = '/home/jovyan/work/datasets/raw/Marxan_DiffMap/Scenario3'\n",
    "MARXAN_INPUTDATA = 'input.dat'\n",
    "\n",
    "userInputFile= readInput(SCEN1_PATH,MARXAN_INPUTDATA)\n",
    "pu= validateFile(MARXAN_FOLDER,MARXAN_INPUTDATA, planningUnits)\n",
    "spec= validateFile(MARXAN_FOLDER,MARXAN_INPUTDATA, conservationFeature)\n",
    "\n",
    "### Modify BLM\n",
    "userInputFile.BLM = 0.01\n",
    "userInputFile.NUMREPS = 100\n",
    "userInputFile_df = pd.DataFrame.from_dict(userInputFile.__dict__, orient='index')\n",
    "userInputFile_df= userInputFile_df.drop('BLOCKDEFNAME')\n",
    "CreateFileFromDF(f'{MARXAN_FOLDER}/{MARXAN_INPUTDATA}',userInputFile_df,inputDatFile)\n",
    "\n",
    "### Modify SPF and prop\n",
    "spec.prop =0.2\n",
    "spec.spf = 1\n",
    "spec.head()\n",
    "CreateFileFromDF(f'{MARXAN_FOLDER}/{userInputFile.INPUTDIR}/{userInputFile.SPECNAME}', spec, conservationFeature)\n",
    "\n",
    "### Lock-in and Lock-out some PUs\n",
    "import random\n",
    "\n",
    "randomlist = []\n",
    "for i in range(0,int(0.3*len(pu))):\n",
    "    n = random.randrange(2,3,1)\n",
    "    randomlist.append(n)\n",
    "\n",
    "l = [0] * (len(pu) - len(randomlist))\n",
    "randomlist.extend(l)\n",
    "random.shuffle(randomlist)\n",
    "len(randomlist)\n",
    "\n",
    "pu['status'] = randomlist\n",
    "CreateFileFromDF(f'{MARXAN_FOLDER}/{userInputFile.INPUTDIR}/{userInputFile.PUNAME}',pu,planningUnits)\n",
    "\n",
    "### Run Marxan\n",
    "execute_marxan(MARXAN_FOLDER)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MARXAN_FOLDER = '/home/jovyan/work/datasets/raw/Marxan_DiffMap/Scenario'\n",
    "scenarios = ['1','2','3']\n",
    "fig = plt.figure(figsize=(10,10))\n",
    "\n",
    "for idx, sol in enumerate(scenarios):\n",
    "    axn = fig.add_subplot(321+idx)\n",
    "    solution = pd.read_csv(f\"{MARXAN_FOLDER}{sol}/output/output_ssoln.csv\") #freq\n",
    "    pu_area = gpd.read_file(f'{MARXAN_FOLDER}{sol}/pu/pulayer.shp')\n",
    "    solution_grid = pu_area.merge(solution,left_on='PUID',right_on = 'planning_unit',how='inner')\n",
    "    solution_grid.plot(ax=axn,column='number', legend=True)\n",
    "    axn.set_title(f'SCENARIO = {sol}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Difference Map function (update 15th June, with Bvariate Map)\n",
    "Categories: \n",
    "+ Never\n",
    "+ Always\n",
    "+ Bivariate cloropleth (each Scenario divided in 4 classes of equal interval 0-0.25-0.5-0.75-1)\n",
    "\n",
    "Follow this example for [bivariate cloroplepth](https://github.com/zorzalerrante/mapas_censo_2017/blob/master/Coropletas%20Bivariable.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spectra\n",
    "import mapclassify\n",
    "import shapely\n",
    "import matplotlib.patheffects as path_effects\n",
    "from matplotlib.colors import rgb2hex\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%run marxan_utils.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## validating files will depend on needs of the DB\n",
    "## Output will depend on the needs of the DB (now ouputing as DF, but could be a dict {pu:category})\n",
    "\n",
    "def diffMap_bivariate(SCEN1_PATH: str,SCEN2_PATH: str, show_count: bool)-> dict:\n",
    "# 1. Read files (input.dat, pu.dat, pu_grid)\n",
    "    MARXAN_INPUTDATA = 'input.dat'\n",
    "    # Input files\n",
    "    input1 = readInput(SCEN1_PATH,MARXAN_INPUTDATA)\n",
    "    input2 = readInput(SCEN2_PATH,MARXAN_INPUTDATA)\n",
    "    \n",
    "    ## Frequency of solutions output file\n",
    "    s1 = validateFile(SCEN1_PATH,MARXAN_INPUTDATA, OutputSsoln)\n",
    "    s2 = validateFile(SCEN2_PATH,MARXAN_INPUTDATA, OutputSsoln)\n",
    "\n",
    "    ## Number of runs\n",
    "    n1 = int(input1.NUMREPS)\n",
    "    n2 = int(input2.NUMREPS)\n",
    "    \n",
    "# 2. Rename fields and merge with grid \n",
    "    # freq = the total number of times a pu has been selected in all solutions\n",
    "    diff= s1.rename(columns={'number':'freq1'})\n",
    "    diff['freq2'] = s2.number\n",
    "\n",
    "    # rel_freq = freq/number of solutions (different scenarios may have different solution number)\n",
    "    diff['rel_freq1'] = round(diff.freq1/n1,2)\n",
    "    diff['rel_freq2'] = round(diff.freq2/n2,2)\n",
    "\n",
    "# 3. Assign categories\n",
    "    # Never selected in S1 & S2\n",
    "    never = list(diff[(diff['rel_freq1'] == 0)   & (diff['rel_freq2'] == 0)].planning_unit.values)\n",
    "\n",
    "    # Always in S1 & S2\n",
    "    always = list(diff[(diff['rel_freq1'] == 1) & (diff['rel_freq2'] == 1)].planning_unit.values)\n",
    "    \n",
    "    # create dict of categories\n",
    "    categories = {'never': never,\n",
    "                  'always':always}\n",
    "    \n",
    "    diff['category']= 'both'\n",
    "    for key in categories.keys():\n",
    "        diff.loc[diff['planning_unit'].isin(categories[key]),'category'] = key\n",
    "    \n",
    "    return diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "SCEN1_PATH = '/home/jovyan/work/datasets/raw/Marxan_DiffMap/Scenario2'\n",
    "SCEN2_PATH = '/home/jovyan/work/datasets/raw/Marxan_DiffMap/Scenario1'\n",
    "GRID_PATH = f'{SCEN1_PATH}/pu/pulayer.shp'\n",
    "diff = diffMap_bivariate(SCEN1_PATH,SCEN2_PATH, show_count =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>planning_unit</th>\n",
       "      <th>freq1</th>\n",
       "      <th>freq2</th>\n",
       "      <th>rel_freq1</th>\n",
       "      <th>rel_freq2</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>never</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12177</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>never</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12176</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>never</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12175</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12174</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>never</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12173</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12174</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12175</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12176</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12177</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12178 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       planning_unit  freq1  freq2  rel_freq1  rel_freq2 category\n",
       "0              12178      0      0        0.0       0.00    never\n",
       "1              12177      0      0        0.0       0.00    never\n",
       "2              12176      0      0        0.0       0.00    never\n",
       "3              12175      0      7        0.0       0.07     both\n",
       "4              12174      0      0        0.0       0.00    never\n",
       "...              ...    ...    ...        ...        ...      ...\n",
       "12173              5      0     15        0.0       0.15     both\n",
       "12174              4      0     14        0.0       0.14     both\n",
       "12175              3      0     11        0.0       0.11     both\n",
       "12176              2      0      7        0.0       0.07     both\n",
       "12177              1      0      4        0.0       0.04     both\n",
       "\n",
       "[12178 rows x 6 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pu_grid = gpd.read_file(f'{GRID_PATH}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff = pu_grid[['PUID','geometry']].merge(diff,left_on='PUID',right_on ='planning_unit',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PUID</th>\n",
       "      <th>geometry</th>\n",
       "      <th>planning_unit</th>\n",
       "      <th>freq1</th>\n",
       "      <th>freq2</th>\n",
       "      <th>rel_freq1</th>\n",
       "      <th>rel_freq2</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>POLYGON ((732060.000 578650.001, 732060.000 58...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((734060.000 578650.001, 734060.000 58...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((736060.000 578650.001, 736060.000 58...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((738060.000 578650.001, 738060.000 58...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.14</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>POLYGON ((740060.000 578650.001, 740060.000 58...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PUID                                           geometry  planning_unit  \\\n",
       "0     1  POLYGON ((732060.000 578650.001, 732060.000 58...              1   \n",
       "1     2  POLYGON ((734060.000 578650.001, 734060.000 58...              2   \n",
       "2     3  POLYGON ((736060.000 578650.001, 736060.000 58...              3   \n",
       "3     4  POLYGON ((738060.000 578650.001, 738060.000 58...              4   \n",
       "4     5  POLYGON ((740060.000 578650.001, 740060.000 58...              5   \n",
       "\n",
       "   freq1  freq2  rel_freq1  rel_freq2 category  \n",
       "0      0      4        0.0       0.04     both  \n",
       "1      0      7        0.0       0.07     both  \n",
       "2      0     11        0.0       0.11     both  \n",
       "3      0     14        0.0       0.14     both  \n",
       "4      0     15        0.0       0.15     both  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = diff.plot(column='rel_freq1', edgecolor='none', scheme='fisherjenks', k=4)\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = diff.plot(column='rel_freq2', edgecolor='none', scheme='fisherjenks', k=4)\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Create palettes\n",
    "n_categories = 4\n",
    "full_palette = sns.color_palette('PiYG', n_colors=(n_categories - 1) * 2 + 1)\n",
    "sns.palplot(full_palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap_x = full_palette[n_categories - 1:]\n",
    "sns.palplot(cmap_x)\n",
    "cmap_y = list(reversed(full_palette))[n_categories - 1:]\n",
    "sns.palplot(cmap_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EqualInterval       \n",
       "\n",
       "  Interval     Count\n",
       "--------------------\n",
       "[0.00, 0.25] |  2518\n",
       "(0.25, 0.50] |  3860\n",
       "(0.50, 0.75] |  4025\n",
       "(0.75, 1.00] |  1775"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scen1_bin = mapclassify.EqualInterval(diff['rel_freq1'], k=n_categories)\n",
    "scen1_bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creamos la figura\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n",
    "\n",
    "# por cada categoría dibujamos en la figura\n",
    "for i in range(n_categories):\n",
    "    areas_in_this_category = np.where(scen1_bin.yb == i)[0]\n",
    "    # usamos el método rgb2hex para que matplotlib reconozca el color que utilizamos\n",
    "    diff.iloc[areas_in_this_category].plot(color=rgb2hex(cmap_x[i]), ax=ax, edgecolor='none')\n",
    "    \n",
    "# sacamos elementos decorativos del gráfico\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EqualInterval       \n",
       "\n",
       "  Interval     Count\n",
       "--------------------\n",
       "[0.00, 0.25] |  6402\n",
       "(0.25, 0.50] |  3244\n",
       "(0.50, 0.75] |  1868\n",
       "(0.75, 1.00] |   664"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scen2_bin = mapclassify.EqualInterval(diff['rel_freq2'], k=n_categories)\n",
    "scen2_bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creamos la figura\n",
    "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n",
    "\n",
    "# por cada categoría dibujamos en la figura\n",
    "for i in range(n_categories):\n",
    "    areas_in_this_category = np.where(scen2_bin.yb == i)[0]\n",
    "    # usamos el método rgb2hex para que matplotlib reconozca el color que utilizamos\n",
    "    diff.iloc[areas_in_this_category].plot(color=rgb2hex(cmap_y[i]), ax=ax, edgecolor='none')\n",
    "    \n",
    "# sacamos elementos decorativos del gráfico\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Combine both palettes\n",
    "cmap_xy = []\n",
    "bivariate_palette = {}\n",
    "\n",
    "for j in range(n_categories):\n",
    "    for i in range(n_categories):\n",
    "        x = spectra.rgb(*cmap_x[i][0:3])\n",
    "        y = spectra.rgb(*cmap_y[j][0:3])\n",
    "        \n",
    "        if i == j and i == 0:\n",
    "            cmap_xy.append(x.darken(1.5).rgb)\n",
    "        elif i == 0:\n",
    "            cmap_xy.append(y.rgb)\n",
    "        elif j == 0:\n",
    "            cmap_xy.append(x.rgb)\n",
    "        else: \n",
    "            blended = x.blend(y, ratio=0.5)\n",
    "            \n",
    "            if i == j:\n",
    "                blended = blended.saturate(7.5 * (i + 1))\n",
    "            else:\n",
    "                blended = blended.saturate(4.5 * (i + 1))\n",
    "                \n",
    "            cmap_xy.append(blended.rgb)\n",
    "            \n",
    "        bivariate_palette[(i, j)] = rgb2hex(cmap_xy[-1])\n",
    "            \n",
    "cmap_xy = np.array(cmap_xy).reshape(n_categories, n_categories, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f499d85dfa0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cmap_xy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0, 0): '#f2f3f2',\n",
       " (1, 0): '#d9f0bc',\n",
       " (2, 0): '#9acd61',\n",
       " (3, 0): '#589b28',\n",
       " (0, 1): '#fad6ea',\n",
       " (1, 1): '#efe3b6',\n",
       " (2, 1): '#c1d58d',\n",
       " (3, 1): '#9abe6a',\n",
       " (0, 2): '#e897c4',\n",
       " (1, 2): '#efbeb8',\n",
       " (2, 2): '#c6b268',\n",
       " (3, 2): '#9e9b55',\n",
       " (0, 3): '#cb3289',\n",
       " (1, 3): '#df89a3',\n",
       " (2, 3): '#c37768',\n",
       " (3, 3): '#b15537'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bivariate_palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n",
    "\n",
    "for i in range(n_categories):\n",
    "    for j in range(n_categories):\n",
    "        areas_in_this_category = set(np.where(scen1_bin.yb == i)[0]) & set(np.where(scen2_bin.yb == j)[0])\n",
    "        diff.iloc[list(areas_in_this_category)].plot(color=bivariate_palette[(i, j)], ax=ax, edgecolor='none')\n",
    "    \n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
    "\n",
    "# ahora hacemos iteraciones anidadas, porque queremos las combinaciones de dos categorías\n",
    "for i in range(n_categories):\n",
    "    for j in range(n_categories):\n",
    "        # buscamos las áreas que tienen ambos valores de categoría\n",
    "        areas_in_this_category = set(np.where(scen1_bin.yb == i)[0]) & set(np.where(scen2_bin.yb == j)[0])\n",
    "        diff.iloc[list(areas_in_this_category)].plot(color=bivariate_palette[(i, j)], ax=ax, edgecolor='none')\n",
    "\n",
    "diff[diff['category']=='always'].plot(color='blue', ax=ax, edgecolor='none') ### add 1-1\n",
    "diff[diff['category']=='never'].plot(color='gray', ax=ax, edgecolor='none') ### add 0-0\n",
    "        \n",
    "ax.set_axis_off()\n",
    "\n",
    "# dibujamos la leyenda. el parámetro de axes es la posición (x, y) y el ancho (width, height) de la leyenda.\n",
    "a = fig.add_axes([.85, .45, .2, .2], facecolor='y')\n",
    "a.imshow(cmap_xy, origin='lower')\n",
    "a.set_xlabel('freq in scenario 1 $\\\\rightarrow$', fontsize='small')\n",
    "a.set_ylabel('freq in scenario 2 $\\\\rightarrow$', fontsize='small')\n",
    "a.set_xticks([])\n",
    "a.set_yticks([])\n",
    "sns.despine(ax=a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Difference Map Function (Old - with many categories)\n",
    "[Reference paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0002586)\n",
    "\n",
    "#### Categories:\n",
    "- never : Never selected in any of the scenarios\n",
    "- always : Always selected in both scenarios (can include locked in areas in both scenarios)\n",
    "- S1_always : Always selected only in S1 (can include locked in areas in S1)\n",
    "- S2_always : Always selected only in S2 (can include locked in areas in S2)\n",
    "- S1_only: Only selected in S1 (excluding pus already in *always* or *S1_always*. This can be representd as frequency on map insted of one color to highlight areas that are selected moreoften)\n",
    "- S2_only: Only selected in S2 (excluding pus already in *always* or *S1_always*. This can be representd as frequency on map insted of one color to highlight areas that are selected moreoften)\n",
    "- both_comparable: PUs selected in both scenarios with comparable frequency [the absolute difference of the relative frequencies is below 10% (relfreqS1 - relfreqS2 < 0.1)]\n",
    "- both_higherS1: PUs selected in both scenarios BUT with higher frequency in S1 [the difference of the relative frequencies is above 10% (relfreqS1 - relfreqS2 > 0.1)]\n",
    "- both_higherS2: PUs selected in both scenarios BUT with higher frequency in S2 [the difference of the relative frequencies is above 10% (relfreqS1 - relfreqS2 < -0.1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "## validating files will depend on needs of the DB\n",
    "## Output will depend on the needs of the DB (now ouputing as DF, but could be a dict {pu:category})\n",
    "\n",
    "def diffMap(SCEN1_PATH: str,SCEN2_PATH: str, show_count: bool)-> dict:\n",
    "# 1. Read files (input.dat, pu.dat, pu_grid)\n",
    "    MARXAN_INPUTDATA = 'input.dat'\n",
    "    # Input files\n",
    "    input1 = readInput(SCEN1_PATH,MARXAN_INPUTDATA)\n",
    "    input2 = readInput(SCEN2_PATH,MARXAN_INPUTDATA)\n",
    "    \n",
    "    #pu.dat files\n",
    "    pu1 = validateFile(SCEN1_PATH,MARXAN_INPUTDATA, planningUnits)\n",
    "    pu2 = validateFile(SCEN2_PATH,MARXAN_INPUTDATA, planningUnits)\n",
    "    \n",
    "    ## Frequency of solutions output file\n",
    "    s1 = validateFile(SCEN1_PATH,MARXAN_INPUTDATA, OutputSsoln)\n",
    "    s2 = validateFile(SCEN2_PATH,MARXAN_INPUTDATA, OutputSsoln)\n",
    "\n",
    "    ## Number of runs\n",
    "    n1 = int(input1.NUMREPS)\n",
    "    n2 = int(input2.NUMREPS)\n",
    "    \n",
    "# 2. Rename fields and merge with grid \n",
    "    # freq = the total number of times a pu has been selected in all solutions\n",
    "    diff= s1.rename(columns={'number':'freq1'})\n",
    "    diff['freq2'] = s2.number\n",
    "\n",
    "    # rel_freq = freq/number of solutions (different scenarios may have different solution number)\n",
    "    diff['rel_freq1'] = round(diff.freq1/n1,2)\n",
    "    diff['rel_freq2'] = round(diff.freq2/n2,2)\n",
    "\n",
    "    # diff = selection of scenario 1 - scenario 2\n",
    "    # dif_abs = absolute difference \n",
    "    diff['diff'] = diff.rel_freq1  - diff.rel_freq2\n",
    "    diff['diff_abs'] = diff['diff'].abs()\n",
    "\n",
    "    # pu.dat get status\n",
    "    pu1 = pu1.rename(columns={'status':'status1'})\n",
    "    pu1['status2'] = pu2.status\n",
    "\n",
    "# 3. Assign categories\n",
    "    # Never selected in S1 & S2\n",
    "    never = list(diff[(diff['rel_freq1'] == 0)   & (diff['rel_freq2'] == 0)].planning_unit.values)\n",
    "\n",
    "    # Locked-in /Always\n",
    "    # status 2 in pu.dat should give rel_freq=1, but that is not the case. TO DO: understand why\n",
    "    lock1 = list(diff[(diff['rel_freq1'] == 1)].planning_unit.values)\n",
    "    lock2 = list(diff[(diff['rel_freq2'] == 1)].planning_unit.values)\n",
    "    always = list(set(lock1) & set(lock2)) # Always selected in both\n",
    "    S1_always = list(set(lock1) - set(always)) # always selected in S1\n",
    "    S2_always = list(set(lock2) - set(always)) # always selected in S2\n",
    "\n",
    "    # Always selected in S1 & S2\n",
    "    #always = list(diff[(diff['rel_freq1'] == 1) & (diff['rel_freq2'] == 1)].planning_unit.values)\n",
    "\n",
    "    # Only selected in S1, but below 1\n",
    "    S1_only = list(diff[(diff['rel_freq1'] != 0 ) & (diff['rel_freq2'] == 0)].planning_unit.values)\n",
    "    S1_only = list(set(S1_only) - set(S1_always))\n",
    "\n",
    "    # Only selected in S2, but below 1\n",
    "    S2_only = list(diff[(diff['rel_freq1'] == 0) & (diff['rel_freq2'] != 0)].planning_unit.values)\n",
    "    S2_only = list(set(S2_only) - set(S2_always))\n",
    "    \n",
    "    # Selected in both with comparable frequency (difference between -0.1 and 0.1)\n",
    "    both_comparable = list(diff[(diff['rel_freq1'] != 0) & \n",
    "                            (diff['rel_freq1'] < 1) &\n",
    "                            (diff['rel_freq2'] != 0) & \n",
    "                            (diff['rel_freq2'] < 1) & \n",
    "                            (diff['diff_abs'] <= 0.1)].planning_unit.values)\n",
    "\n",
    "    # Selected in both with higher frequency in S1 (difference > 0.1)\n",
    "    both_higherS1 = list(diff[(diff['rel_freq1'] != 0) & \n",
    "                            (diff['rel_freq1'] < 1) &\n",
    "                            (diff['rel_freq2'] != 0) & \n",
    "                            (diff['rel_freq2'] < 1)  & \n",
    "                            (diff['diff'] > 0.1)].planning_unit.values)\n",
    "\n",
    "    # Selected in both with higher frequency in S2 (difference < -0.1)\n",
    "    both_higherS2 = list(diff[(diff['rel_freq1'] != 0) & \n",
    "                            (diff['rel_freq1'] < 1) &\n",
    "                            (diff['rel_freq2'] != 0) & \n",
    "                            (diff['rel_freq2'] < 1)  & \n",
    "                            (diff['diff'] < -0.1)].planning_unit.values)\n",
    "    \n",
    "    # create dict of categories\n",
    "    categories = {'never': never,\n",
    "         'always':always,\n",
    "         'S1_always':S1_always,\n",
    "         'S2_always':S2_always,\n",
    "         'S1_only':S1_only,\n",
    "         'S2_only':S2_only,\n",
    "         'both_comparable':both_comparable,\n",
    "         'both_higherS1':both_higherS1,\n",
    "         'both_higherS2':both_higherS2}\n",
    "    \n",
    "    diff['category']= 'NotAssigned'\n",
    "    for key in categories.keys():\n",
    "        diff.loc[diff['planning_unit'].isin(categories[key]),'category'] = key\n",
    "        \n",
    "# 4. Sanity checks\n",
    "# All pu's are assigned to one category\n",
    "    if show_count:\n",
    "        all_pu= []\n",
    "        for i in categories.keys():\n",
    "            all_pu = all_pu + categories[i]\n",
    "            print(f\"there are {len(categories[i])} pu's assigned to {i}\")\n",
    "        print(f\"there are {len(diff)-len(all_pu) } pu's not assigned\")\n",
    "    \n",
    "    # Covert to dict (this format can be more elaborate depending on FE requirements)\n",
    "    diff_dict =diff[['planning_unit','category','diff','diff_abs']].to_dict()\n",
    "    \n",
    "    return diff_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotDiffMap(diff_dict: dict,GRID_PATH: str, solid: bool):\n",
    "    # grid file\n",
    "    pu_grid = gpd.read_file(f'{GRID_PATH}')\n",
    "    \n",
    "    # convert dict to dataframe\n",
    "    diff = pd.DataFrame.from_dict(diff_dict,orient='columns')\n",
    "    \n",
    "    # merge with pu grid\n",
    "    diff = pu_grid[['PUID','geometry']].merge(diff,left_on='PUID',right_on ='planning_unit',how='inner')\n",
    "    \n",
    "    # Plot as solid colors or frequency \n",
    "    diff.plot(column = 'category',legend =True,figsize =(10,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 829 pu's assigned to never\n",
      "there are 10 pu's assigned to always\n",
      "there are 256 pu's assigned to S1_always\n",
      "there are 41 pu's assigned to S2_always\n",
      "there are 4924 pu's assigned to S1_only\n",
      "there are 189 pu's assigned to S2_only\n",
      "there are 946 pu's assigned to both_comparable\n",
      "there are 4371 pu's assigned to both_higherS1\n",
      "there are 612 pu's assigned to both_higherS2\n",
      "there are 0 pu's not assigned\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SCEN1_PATH = '/home/jovyan/work/datasets/raw/Marxan_DiffMap/Scenario2'\n",
    "SCEN2_PATH = '/home/jovyan/work/datasets/raw/Marxan_DiffMap/Scenario3'\n",
    "GRID_PATH = f'{SCEN1_PATH}/pu/pulayer.shp'\n",
    "diff = diffMap(SCEN1_PATH,SCEN2_PATH, show_count =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Difference Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Each category as solid color\n",
    "plotDiffMap(diff,GRID_PATH, solid= True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot some categories as solid value and some as frequency \n",
    "\n",
    "fig, ax = plt.subplots(1,1,figsize=(15,15))\n",
    "ax.set_aspect('equal')\n",
    "divider = make_axes_locatable(ax)\n",
    "ax1 = divider.append_axes(\"right\", size=\"5%\", pad=0.1)\n",
    "ax2 = divider.append_axes(\"right\", size=\"5%\", pad=0.3)\n",
    "ax3 = divider.append_axes(\"right\", size=\"5%\", pad=0.4)\n",
    "ax4 = divider.append_axes(\"right\", size=\"5%\", pad=0.5)\n",
    "\n",
    "diff[diff['planning_unit'].isin(never)].plot(ax = ax, color = 'black',legend=True, label = 'Never')\n",
    "diff[diff['planning_unit'].isin(lock_both)].plot(ax = ax, color = 'yellow',legend =True, label='Always')\n",
    "diff[diff['planning_unit'].isin(only_lock1)].plot(ax = ax, color = 'red',legend =True, label='Always S1')\n",
    "diff[diff['planning_unit'].isin(only_lock2)].plot(ax = ax, color = 'purple',legend =True, label ='Always S2')\n",
    "diff[diff['planning_unit'].isin(only1)].plot(ax = ax, column = 'diff',cmap = 'Oranges',vmin=0, vmax=1, legend= True, cax=ax1)\n",
    "diff[diff['planning_unit'].isin(only2)].plot(ax = ax, column = 'diff_abs',cmap = 'Purples',vmin=0, vmax=1,legend=True,cax=ax2)\n",
    "diff[diff['planning_unit'].isin(both_comparable)].plot(ax = ax, color = 'gray',legend = True)\n",
    "diff[diff['planning_unit'].isin(both_higherS1)].plot(ax = ax, column = 'diff',cmap = 'Reds',vmin=0, vmax=1,legend=True,cax=ax3)\n",
    "diff[diff['planning_unit'].isin(both_higherS2)].plot(ax = ax, column = 'diff_abs', cmap= 'Blues',vmin=0, vmax=1,legend=True,cax=ax4)\n",
    "diff[diff['planning_unit'].isin(lock2)].plot(ax = ax, color = 'green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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